Computing early adopters and potential influencers using transactional data and network analysis

ABSTRACT

The early adopters and potential influencers (EAPI) system, method and computer-readable medium provide a way to identify early adopters and potential influencers. The EAPI system obtains a list of purchases for customers of merchants and/or subscriptions from a transaction tracking system. The EAPI system creates a time-based transaction network, and using a scoring function, the EAPI system determines an early adopter and/or potential influencer ranking among customers in the network. The EAPI system may use one or more customer attributes to determine a customer&#39;s influence with respect to different dimensions.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 61/491,154, filed May 27, 2011, which is also incorporatedherein by reference in its entirety.

TECHNICAL FIELD

The present description relates to how to efficiently and effectively toidentify early adopters and potential influencers for merchants. Morespecifically, the present description relates to a way to determine anearly adopter and/or potential influencer ranking among customers usinga time-based transaction network and using a scoring function.

BACKGROUND

Merchants desire a way to determine the customers to offer deals thatreturn the greatest return on investment. Currently, merchants offerdeals to customers without the ability to determine the return themerchant should expect to realize from the deals offered.

SUMMARY

The early adopters and potential influencers (EAPI) system, method andcomputer-readable medium obtain a list of transactional data, determinetransactional networks using gathered transactions and external data,and score customers identified in transactional activities in thetransactional networks. The transactional data may include commercialtransactions that identify one or more purchases of products and/orservices by a customer from a merchant and/or one or more subscriptions.The merchant may refer to a company that offers a product and/or servicefor purchase and/or lease.

The EAPI system uses transactional networks to construct networks (e.g.,network diagrams) that provide customers information and transactions ofthe customers over time. The transactional networks include weighted anddirected, weighted and non-directed, non-weighted and non-directednetworks computed with information regarding previous transactionalactivities of transaction generating entities (e.g., customers, publicand private companies, non-profit organizations, and governmentalinstitutions).

The previous transactional activities include previous behavioral datagathered using a manual or automatic information system. The behavioraldata includes information from which behavior properties of thetransaction generating entity can be extracted using a patternrecognition algorithm. The pattern recognition algorithm may includeand/or relates to unsupervised learning, supervised learning,semi-supervised learning, reinforcement learning, association ruleslearning, Bayesian learning, solving for probabilistic graphical models,among other computational intelligence algorithms that may use aninteractive process to extract patterns from data. The behaviorproperties refer to information that associates actions of the customerover time and/or space (e.g., geographical information) from thetransaction generating entity.

The space refers to a geographical space denoted by latitude andlongitude coordinates, and/or a network space with relationship betweencustomers. The network space refers to respective social networksextracted from social networking systems.

The social networking systems include online social network websites,virtual communities of practice, and virtual communities of interest,among other social network services. The transaction network analysisframework may identify potential influencers and early adopters relatedto each other based on respective relationships of the potentialinfluencers and early adopters with transactions with the merchants. Thetransactional data may include commercial transaction information thatrelates to a purchase of a product and/or service from a merchant and/ora subscription.

The EAPI system distinguishes between different objects within anetwork. The objects include nodes of the network, using general objectranking algorithms. The EAPI system computes linking analysis measuresfor the customers and the merchants, identifies most influentialcustomers among the customers, and identifies merchants trending amongthe customers identified as most influential. The general object rankingalgorithms rank objects in multi-modal networks including pop-rank. Thelinking analysis measures include centrality measures and/or relatednetwork analysis measures that provides analysis identifying differencesbetween the nodes within the network. Centrality measures measure anodes importance or prominence in the network. The more central a nodeis in a network the more significant the node is as an influencer (e.g.,aid in the spread of information about a merchant by a customer and thegoodwill of the merchant).

The EAPI system computes centrality measures from transactional data fortransactional networks, classifies customers into different segments byapplying data clustering strategies and pattern recognition algorithmson the transactional data, and clusters the customers according to thetransactional data.

Other systems, methods, and features will be, or will become, apparentto one with skill in the art upon examination of the following figuresand detailed description. It is intended that all such additionalsystems, methods, features and be included within this description, bewithin the scope of the disclosure, and be protected by the followingclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The system may be better understood with reference to the followingdrawings and description. Non-limiting and non-exhaustive descriptionsare described with reference to the following drawings. The componentsin the figures are not necessarily to scale, emphasis instead beingplaced upon illustrating principles. In the figures, like referencednumerals may refer to like parts throughout the different figures unlessotherwise specified.

FIG. 1 shows the early adopters and potential influencers (EAPI) systemconfiguration.

FIG. 2 shows logic flow the EAPI system may use to determine the degreeof influence for customers.

FIG. 3 shows customers and merchants as nodes of a transactionalnetwork.

FIG. 4 shows transactions for a customer c₁ located at merchants m₁ andm₃.

FIG. 5 shows transactions for multiple customers located at multiplemerchants.

FIG. 6 shows networked nodes of degrees of influence of customers.

FIG. 7 shows other components of the EAPI system configuration.

DETAILED DESCRIPTION

The principles described herein may be embodied in many different forms.Not all of the depicted components may be required, however, and someimplementations may include additional, different, or fewer components.Variations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the claims as set forthherein. Additional, different or fewer components may be provided.

The early adopters and potential influencers (EAPI) system, method andcomputer-readable medium provide a way to compute an influence and earlyadoption ranking from a list of customers, using the informationgathered from a transaction tracking system. The influence and earlyadoption ranking and/or score can be associated with respect to amerchant and/or product over time.

Using information collected, for example using Application ProgrammingInterfaces (APIs) from online social networking websites and other datasources (e.g., third party applications), the EAPI system characterizesthe information associated with the transactions, and extends theanalysis of the influencers. The EAPI system may providecharacterizations in terms of customers, transactions and productsand/or merchants.

The EAPI system may use spatial information (e.g., geographicalinformation) and/or other dimensions to enhance the scoring of customersas potential early adopters and potential influencers. The EAPI systemclusters transactions, customers, and/or merchants to refine the scoringfor early adoption or potential influence. The EAPI system may also useinformation such as the social network for a customer and/or merchant,to determine further improvements on the influence and early adoptionranking computation.

Influence is “the capacity of causing an effect in indirect orintangible ways,” as defined according to Merriam-Webster. The EAPIsystem associates the computed influence and early adoption ranking withan influence proxy, where the score of the influence proxy score may beinterpreted as the degree of influence with respect to fellow shoppers(e.g., customers).

The EAPI system may use a transaction tracking system to gathernecessary data, in which the data may be gathered from various sources(e.g., merchant processors, acquiring banks, issuing banks and retailersdirect feeds and credit card networks) using different data collectionmechanisms.

FIG. 1 shows the EAPI system configuration 100. The EAPI systemconfiguration 100 includes an EAPI system 102. The EAPI system 102includes a communications interface 104 used to communicate with variouscomponents of the EAPI system configuration 100 through a network 106(e.g., the Internet). The communications interface 104 is coupled to aprocessor 108 coupled to a memory 110. The EAPI system 102 obtains alist of transactional data 112, determines transactional networks 114using gathered transactions and external data, and scores customers 116identified in transactional activities in the transactional networks114. The transactional data 112 may include commercial transactions thatidentify one or more purchases of products and/or services by a customer116 from one or more merchants 118 and/or one or more subscriptions 120.The merchant 118 may refer to a company that offers a product and/orservice for purchase and/or lease.

In one embodiment, the EAPI system 102 generates, for one, some or allcustomers scoring equal to or greater than a scoring threshold value, apreprocessed transaction for a merchant, wherein the scoring thresholdvalue determines the influence of the customer with respect to othercustomers. The preprocessed transaction may include customer informationthat a merchant may use to expedite processing of a sales transactionwith the customer, including customer identifiers, payment methods, anddiscounts and deals the customer is eligible to use for purchases. TheEAPI system 102 processes the preprocessed transaction for the merchantwhen the customer commences a transaction with the merchant. Processingthe preprocessed transaction reduces the elapsed time for the customerto complete the commenced transaction.

In another embodiment, the EAPI system 102 may determine for thecustomer scoring equal to or greater than the scoring threshold value,when to generate a preprocessed transaction for a merchant by evaluatinga time period in which the customer is expected to commence atransaction for a product or service offered by the merchant, and/orevaluating a geographical distance to the merchant within which thecustomer is expected to be during another time period. The EAPI system102 may offer the customer a deal with a merchant based on identifyingthe merchant with whom the customer scoring is equal to or greater thana scoring threshold value, wherein the scoring threshold valuedetermines the influence of the customer with respect to othercustomers.

The EAPI system 102 may also calculate a number of influenced customersinfluenced by the customers scoring equal to or greater than a scoringthreshold value identified as the influencers for a merchant, identify aleast number of the influencers that provide the greatest number ofinfluenced customers, and offer the least number of the influencers adeal with a merchant. The EAPI system 102 may alternatively calculate anumber of influenced customers influenced by the customers scoring equalto or greater than the scoring threshold value identified as theinfluencers for a merchant, identify a least number of the influencersthat provide the greatest number of influenced customers common to theleast number of the influencers, and offer the least number of theinfluencers a deal with a merchant. The EAPI system 102 may furthercalculate a number of influenced customers influenced by the customersscoring equal to or greater than the scoring threshold value identifiedas the influencers for a merchant, identify a least number of theinfluencers that provide the greatest number of influenced customersunique to each of the least number of the influencers, and offer theleast number of the influencers a deal with a merchant

The EAPI system 102 uses the transactional networks 114 to constructnetworks (e.g., network diagrams) that provide customers 122 informationand transactions of the customers over time. The transactional networks114 may include weighted and directed, weighted and non-directed,non-weighted and non-directed networks computed with informationregarding previous transactional activities of transaction generatingentities (e.g., customers, public and private companies, non-profitorganizations, and governmental institutions).

The previous transactional activities include previous behavioral data124 gathered using a manual or automatic information system. Thebehavioral data 124 includes information from which behavior properties126 of the transaction generating entity can be extracted using one ormore pattern recognition algorithms 128. The pattern recognitionalgorithms 128 may include and/or relate to unsupervised learning,supervised learning, semi-supervised learning, reinforcement learning,association rules learning, Bayesian learning, solving for probabilisticgraphical models, among other computational intelligence algorithms thatmay use an interactive process to extract patterns from data. Thebehavior properties 126 refer to information that associates actions ofthe customer over time and/or space (e.g., geographical information 130)from the transaction generating entity.

The space refers to a geographical space denoted by latitude andlongitude coordinates, and/or a network space with relationship betweencustomers 116. The network space refers to respective social networksextracted from social networking systems 132.

The social networking systems 132 include online social networkwebsites, virtual communities of practice, and virtual communities ofinterest, among other social network services. The transaction networkanalysis framework used by the EAPI system 102 may identify potentialinfluencers and early adopters related to each other based on respectiverelationships of the potential influencers and early adopters withtransactions with the merchants 118.

The EAPI system distinguishes between different objects within anetwork. The objects include nodes of the network, using general objectranking algorithms 134. The EAPI system computes linking analysismeasures 136 for the customers and the merchants, identifies mostinfluential customers among the customers, and identifies merchantstrending among the customers identified as most influential. The generalobject ranking algorithms rank objects in multi-modal networks includingpop-rank. Pop-rank is a method that extends the page-rank towardsmulti-graphs with different weights and relationships. The linkinganalysis measures 136 include centrality measures and/or related networkanalysis measures that identify differences between the nodes within thenetwork. Centrality measures measure a nodes importance or prominence inthe network. The more central a node is in a network the moresignificant the node is as an influencer (e.g., aid in the spread ofinformation about a merchant by a customer and the goodwill of themerchant).

The EAPI system computes centrality measures from transactional data fortransactional networks, classifies customers into different segments byapplying data clustering strategies and pattern recognition algorithmson the transactional data, and clusters the customers according to thetransactional data.

The EAPI system uses one or more algorithms to determine the degree ofinfluence 138 for customers. The EAPI system may use the degree ofinfluence 138 to determine the influence and early adoption rankings 140for the customers. The influence and early adoption rankings may includemultiple components, including an influence ranking component 142 and anearly adoption ranking component 144.

FIG. 2 shows logic flow 200 the EAPI system may use to determine thedegree of influence for customers. The transaction tracking system maygather information using at least a transaction date d, merchantidentifier m, and customer identifier c (202). The EAPI system usestransaction data as input, and extended analysis of the transaction datawith a set of attributes V={v₁, . . . , v_(|V|)}. Each transaction maybe determined by the tuple t={d, c, m, V}, where |V|>=0. The EAPI systemmay substitute merchants with products information to accurately computethe influence and early adoption ranking (e.g., influence score) that acustomer has with respect to other customers. The influence and earlyadoption ranking may include multiple components, including an influenceranking component and an early adoption ranking component.

The information used to determine the influence score is based on a setof tuples T={t₁, . . . , t_(|T|)} that may be gathered using transactioncollection systems. Using tuples T, the EAPI system determines acollection of customers C={c₁, . . . , c_(|C|)} by processing the set oftransactions T ordered with respect to customers c∈C, where c is anelement of the set C. The EAPI system determines with the transactions aset of merchants M={m₁, . . . , m_(|M|)} and a time series for eachinformation set and a combination of the customers, merchants, merchantsfor a customer, and customers for a merchant.

The EAPI system uses one or more algorithms to determine the influencedegree of customers. The EAPI system computes, for one, some, or eachcustomer, transaction networks with the information available from thetransaction tracking system. The EAPI system uses previously computednetworks to compute rankings for each customer. The EAPI systemsummarizes the computed rankings with information fusion strategies, inorder to compute an overall score for the influence and early adoptionfor the customers. The overall score is used to rank the customers anddetermine the influence and early adoption influence proxy.

The EAPI system determines for each customer c a baseline transactionnetwork G_(c,0)=(N_(c,C), N_(c,M)) associated with each customer'stransactions. The EAPI system builds the transaction network using amulti-modal network architecture which analyzes a set of nodesN_(c)=(N_(c,C), N_(c,M)) with both customers (N_(c,C)) and merchants(N_(c,M)). The EAPI system determines the edges by the interactionbetween customers and merchants.

If a customer c∈C generates a transaction with a merchant m∈M, the EAPIsystem adds a directed edge e_(cm) to the network. The edge is directedtowards customer c taking as origin the merchants to which the EAPIsystem tracks a transaction.

For each customer, the EAPI system computes a fellow buyers set B_(c)which represents a set of customers who generated a transaction inmerchant m but after customer c. For customers in B_(c), the EAPI systemdetermines directed edges that the EAPI system adds to the network ifand only if the transactions of the directed edges are after the onegenerated by customer c. The EAPI system analyzes the direction of theedge from the customer to the merchants where a transaction wasgenerated.

The EAPI system analyzes the previously presented network as thebaseline network, and analyzes weights as the frequency of transactionsthat customer c has with merchant m.

For each customer (204), the EAPI system uses the vector of attributesV_(c) to extend the previous graph with richer information and a moreaccurate representation of the transaction network. For each attribute,the EAPI system uses the information regarding the component of theattribute v∈V_(c), as a weight factors for the edges in E_(c). The EAPIsystem builds, for each v∈V_(c), a new network G_(v,c)=(N_(c), E_(v,c)).

As mentioned previously, different weights can be determined accordingto different types of information available represented by v_(i)∈V_(c).If v_(i) is a transactional information source that states a degreewhich is proportional to the value of the transaction (e.g., theamount), the EAPI system weights the edge e_(c,m) with v_(i). If theattribute is a set of coordinates, the EAPI system determines theweights of the edge using spatial-based criteria. As an example, ifv_(i) is associated with the coordinates of merchants, the EAPI systemmay analyze the weight as the inverse of the distance with respect tothe following transaction if and only if the transaction is committed ina different merchant (e.g., the longer the distance, the smaller theinfluence that a customer could bring nearby that area).

The EAPI system uses a fusion of ranking algorithms to analyze previousrepresentations of the transaction networks. For each customer, the EAPIsystem determines a set of networks G_(c)={G_(c,0), G_(c,v1), . . . ,G_(c,v|V|)} (206). For each network in G_(c) (208), the EAPI systemcomputes a link analysis score method s for each merchant m andrepresents the link analysis score method s by set S={s₁, . . . ,s_(|S|)} (210). The link analysis score methods used by the EAPI systemmay include pagerank, weighted pagerank, HITS hubs, HITS centrality,weighted HITS, clustering coefficient, and/or eigenvector centrality.Hyperlink-Induced Topic Search (HITS) (also referred to as hubs andauthorities) is a link analysis algorithm that rates web pages. HITScentrality provides a list of authority and hub centralities for thevertices of a graph.

The EAPI system computes the link analysis measures, and uses aninformation fusion criteria F_(s): R^(|S|)→R to summarize the scoringinformation and final representation of the degree of influence that thecustomer c has over a particular transactional network g∈G_(c). Theinformation fusion criteria denote a new score that is associateddirectly with a network, computed for the transactional networks g∈G_(c)(212).

For the summarized scores obtained in each graph, the EAPI system mayuse another information fusion criteria F_(g): R^(|Gc|)→R to determinethe degree of influence and early adoption that a customer has overother customers (214).

The EAPI system uses previously computed networks to compute rankingsfor each customer. The EAPI system summarizes the computed rankings withinformation fusion strategies, in order to compute an overall score forthe influence and early adoption for the customers (216). The overallscore is used to rank the customers and determine the influence andearly adoption influence proxy.

The relevance and accuracy of the computations computed by the EAPIsystem is directly related to the set of weighted representationscomputed for the transaction network G_(c,v1), . . . , G_(c,v|V|), andthe set of centrality measures s∈S analyzed. Also, the EAPI systemaccepts external influence ratings in order to complement the finalscoring computed by the EAPI system. If for each merchant, an externalinfluence scoring system (e.g., delivered by an online social mediaanalysis tool) is available that provides an external influence score,the EAPI system may analyze the external influence score as a newparameter in the set of link analysis measures S, and integrate into theEAPI system by information fusion functions.

The EAPI system provides actions (e.g., options) for the customers basedon the customer rankings (218). The actions (e.g., options) provided bythe EAPI system may include offering deals to the customers with thegreatest influence (e.g., degree of influence) and/or early adoptionranking.

The EAPI system may use an influencer ranking threshold and/or earlyadopter ranking threshold to determine the customers to whom to offerdeals. For example, a promotion system may receive as an input theinfluencer ranking threshold and/or the early adopter ranking threshold.The promotion system may use the influencer ranking threshold and/or theearly adopter ranking threshold (either alone or in combination withother attributes of the customer) in order to evaluate whether to send apromotion to the specific customer. In one embodiment, the evaluationmay include an expectation value that the specific customer will acceptthe promotion if offered. In another embodiment, the evaluation mayinclude an expected revenue generated from the specific customer (whichmay comprise a multiplication of the expectation value that the specificcustomer will accept the promotion if offered with the amount of revenuegenerated by the specific customer using the promotion). In stillanother embodiment, the evaluation may include an expected amount ofrevenue generated from the specific customer as well as other customers.As discussed below, the EAPI system may determine a multiple of thereturn on investment (ROI). This multiple may be used to determine anexpectation value of revenue generated from the specific customer aswell as other customers influenced by the specific customer. The EAPIsystem may further determine the customers that will completetransactions with merchants regardless of whether the customer isoffered a deal so that the merchant may decide whether to offer thecustomer a deal anyway because the customer is an influencer and/orearly adopter. In one embodiment, the promotion system is separate fromthe EAPI system. In an alternate embodiment, the promotion system isintegrated with the EAPI system.

The EAPI system may identify and distinguish a customer as an influencerand/or early adopter for the merchant and/or the promotion system. TheEAPI system may analyze the customers of the merchant and/or thepromotion system (separately or in combination). The EAPI system maydetermine an effect of a specific customer to influence other customer'sbehavior (such as to influence other customers vis-à-vis the merchant(e.g., the specific customer to influence other customers to purchasefrom the merchant) and/or to influence other customers vis-à-vis thepromotion system (e.g., the specific customer to influence othercustomers to purchase from the promotion system)). For example, the EAPIsystem may determine that a customer of a merchant and the promotionsystem is an influencer for other customers to purchase from themerchant, although the customer is not an influencer for other customersto purchase deals from the promotion system. In this example, the offerof the promotion to the specific customer may provide an added benefitto the merchant (such as in terms of motivating other customers to shopat the merchant without the promotion system). As another example, theEAPI system may determine that the customer of the merchant and thepromotion system is an influencer for other customers to purchase fromboth the merchant and the promotion system. In this example, the offerof the promotion to the specific customer may provide an added benefitto the promotion system (such as in terms of motivating other customersto shop at the promotion system). The influence ranking component 142may include a merchant influencer ranking component and a promotionsystem influencer ranking component, and the early adoption rankingcomponent 144 may include a merchant early adoption ranking componentand a promotion system early adoption ranking component. The EAPI systemmay identify (e.g., rank) the customer using a merchant influencerranking and/or a merchant early adopter ranking and a promotion systeminfluencer ranking and/or a promotion system early adopter ranking.

The EAPI system and/or the promotion system may use the rankings todetermine an effective cost of a deal to the merchant based on theexpected rate of return for the customer based on the influencer rankingthreshold and/or the early adopter ranking threshold. The EAPI systemand/or the promotion system may present deals for the merchant to offercustomers that satisfy an effective cost of the deal desired by themerchant. For example, the merchant may agree to offer deals generatedby the promotion system to customers determined to provide a neutraleffective cost (e.g., the deal costs $20 and based on the influencerranking threshold and/or the early adopter ranking threshold of thecustomer the customer is expected to return at least $20 in revenue tothe merchant).

The EAPI system and/or the promotion system may offer a lower cost forthe deal (e.g., $15 instead of $20) to the merchant where the customersatisfies the merchant influencer ranking and/or the merchant earlyadopter ranking and the promotion system influencer ranking and/or thepromotion system early adopter ranking (so that both the merchant andthe promotion system realize an expected return for offering the deal tothe customer).

The EAPI system may determine for a merchant, for each customeridentified meeting or exceeding the early adopter threshold, a multipleof the return on investment (ROI) realized by one or more advertisingmethods. For example, a deal that costs the merchant Y dollars offeredto a customer identified as an early adopter may return a factor of Ndollars of revenue that the merchant would otherwise realize byadvertising. In this way, the EAPI system provides the merchant theoption to offer deals to early adopters that satisfy the advertisinggoals (e.g., advertising budget and ROI requirements) of merchants bymeeting or exceeding the early adopter threshold, even in thoseinstances where the customer may not meet or exceed the influencerthreshold.

In one implementation, targeting the customers identified as influencerswith deals over the customers identified as early adopters may determinethe success or failure of the campaign. In the event the targetingstrategies are unbalanced in terms of the perceived benefits for theinfluencers and early adopters may result in campaign failures. Forexample, saturation of the market by a handful of influencers (e.g.,users with a large number of followers that propagates and most likelyto make the campaign viral) may leave no room offering early adoptersspecial benefits (e.g., deals), result in dejecting early adopters, andcause a potential decrease in the number of customers needed to take thecampaign into the mainstream.

The EAPI system may determine for a merchant, for each customeridentified by the EAPI system that meet or exceed the influencerthreshold, a multiple of the return on investment for each deal offeredto the customer. For example, the EAPI system may offer a deal to acustomer identified as an influencer that the EAPI system determines anexpectation to provide a return factor of N dollars multiplied by thenumber of additional customers realized by the merchant by offering theinfluencer the deal.

The EAPI system may determine the return on investment for the variouspermutations for each customer of early adopter and influencer includingthe return for customers identified as early adopters and influencers,identified as early adopters, identified as influencers only, andneither early adopter nor influencer.

FIG. 3 shows customers (302, 304, 306, 308, 310) and merchants (312,314, 316) as nodes of a transactional network 300. The EAPI systemdetermines the baseline network G_(c,0) that corresponds to thetransactions network. The EAPI system computes the final score forcustomer c₁. The EAPI system creates the network by placing a bi-partitegraph using customers and merchants, as shown in FIG. 3 as node 302associated with customer c₁.

The EAPI system analyzes the set of transactions as tuples T={(c₁, m₁,t₁), (c₁, m₃, t₂), (c₂, m₃, t₃), (c₃, m₁, t₄), (c₄, m₁, t₅), (c₅, m₂,t₆), (c₆, m₁, t₇)}. The EAPI system uses tuples to represent the mostbasic definition of transactions, and the whole algorithm can beextended by using the transaction amount. The set of customers includeC={c₁, . . . , c₆} and the set of merchants include M={m₁, m₂, m₃}, thetransaction timestamps include t₁, . . . , t₇, where for example t₁< . .. <t₇. The EAPI system analyzes the information related to the distancematrix {d_(ij)}, i∈{1, . . . , 6}, j∈{1, 2, 3} with the walking distancefrom each customer to each merchant, and a matrix {r_(ij)}, i∈{1, . . ., 6}, j∈{1, 2, 3} with the number of check-ins using a social networkfrom each customer in each merchant. These values are referred to aboveas features in the set V for other types of information relevant forcomputing the degree of early adoption and influence of the user (e.g.,customer).

Recalling the logic flow of FIG. 2 , for each customer, the EAPI systemgenerates several transactional networks, and computes for each networkcentrality measures. The EAPI system aggregates the centrality measuresper network using an information fusion strategy. The EAPI system maybase these measures on a wide range of options as discussed above. TheEAPI system may aggregate the computed metrics into one final score thatrepresents each customer's score (e.g., the influence and early adoptionranking which may include multiple components, including an influenceranking component and an early adoption ranking component).

FIG. 4 shows transactions 400 for a customer c₁ 302 located at / withone or more merchants, such as m₁ 312 and m₃ 316. The EAPI system placesthe transactions (402, 404) for customer c₁ 302 in the network.

FIG. 5 shows transactions 500 for multiple customers located at multiplemerchants m₁ 312, m₂ 314 and m₃ 316. Once the EAPI system places thecustomer c₁ 302 and the transactions (402, 404) of the customer c₁ 302in the network, the EAPI system incorporates into the networktransactions (502, 504, 506, 508, 510) from other customers (304, 306,308, 310) that occurred after the transactions (402, 404) of customer c₁302.

Once the EAPI system builds (e.g., generates) the network for node c₁302, the EAPI system computes centrality values. The EAPI systemexecutes n algorithms that provide the results for node c₁ 302 thatinclude S₀={s_(0,1), . . . , s_(0,n)}. The EAPI system may use aninformation fusion technique represented by the function F_(s) todetermine the score for c₁ 302 in the baseline transactional network asS_(0,c1)=F_(s)(s_(0,1), . . . , S_(0,n)).

The EAPI system, for the following networks, then considers theadjacency matrices available for the social network check-ins and thewalking distance information. The EAPI system may build a completebi-partite graph by analyzing the values from both matrices as theweights for each network edge. The EAPI system may build (e.g.,generate) networks G_(c1,v1) and G_(c1,v2), where for each network, theEAPI system determines n_(v1) and n_(v2) different centrality measuresfor customer c₁ 302. The EAPI system determines a set of scoresS₁={s_(1,1), . . . , s_(1,nv1)} and S_(2,1)={s_(2,1), . . . ,s_(2,nv2)}. The EAPI system may then use an information fusion techniquerepresented by the functions F_(s,v1) and F_(s,v2) to determine thescore for customer c₁ 302 in both networks as S_(1,c1)=F_(s,v1){s_(1,1),. . . , s_(1,n)} and S_(2,c1)=F_(s,v2){s_(2,1), . . . , s_(2,n)}

The EAPI system determines the final score for customer c₁ 302 byaggregating the previous scores with a new information fusion techniquerepresented by function F_(g), so that S_(c1)=F_(g)(S_(0,c1), S_(1,c1),S_(2,c1)). The EAPI system repeats the process for each customer, anddetermines the final list of ranked customers by using scores S_(c1), .. . , S_(c5).

FIG. 6 shows networked nodes 600 of degrees of influence of customers.The degrees of influence of customers (602, 604, 606, 608, 610). Thedegrees of influence of customers (602, 604, 606, 608, 610) may berepresented by the size (e.g., larger the nodes the greater theinfluence) and/or color of the nodes in the network. The proximity ofthe nodes to each other may further indicate the degree of influence ofthe customers, as represented by the length of the lines (614, 616, 618,620, 622, 624, 626) between the nodes. For example, the shorter thelength of the line between customer nodes the stronger the influence ofthe customer node represented by the larger size of the node.

FIG. 7 shows other components of the system configuration. The EAPIsystem may be deployed as a general computer system used in a networkeddeployment. The computer system may operate in the capacity of a serveror as a client user computer in a server-client user networkenvironment, or as a peer computer system in a peer-to-peer (ordistributed) network environment. The computer system may also beimplemented as or incorporated into various devices, such as a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (FDA), a mobile device, a palmtop computer, a laptop computer,a desktop computer, a communications device, a wireless telephone, aland-line telephone, a control system, a camera, a scanner, a facsimilemachine, a printer, a pager, a personal trusted device, a web appliance,a network router, switch or bridge, or any other machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. In a particular embodiment, thecomputer system may be implemented using electronic devices that providevoice, video or data communication. Further, while a single computersystem may be illustrated, the term “system” shall also be taken toinclude any collection of systems or sub-systems that individually orjointly execute a set, or multiple sets, of instructions to perform oneor more computer functions.

The computer system may include a processor 702, such as, a centralprocessing unit (CPU), a graphics processing unit (GPU), or both. Theprocessor may be a component in a variety of systems. For example, theprocessor may be part of a standard personal computer or a workstation.The processor may be one or more general processors, digital signalprocessors, application specific integrated circuits, field programmablegate arrays, servers, networks, digital circuits, analog circuits,combinations thereof, or other now known or later developed devices foranalyzing and processing data. The processors and memories discussedherein, as well as the claims below, may be embodied in and implementedin one or multiple physical chips or circuit combinations. The processormay execute a software program, such as code generated manually (i.e.,programmed).

The computer system may include a memory 704 that can communicate via abus. The memory may be a main memory, a static memory, or a dynamicmemory. The memory may include, but may not be limited to computerreadable 710 storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In onecase, the memory may include a cache or random access memory for theprocessor. Alternatively or in addition, the memory may be separate fromthe processor, such as a cache memory of a processor, the memory, orother memory. The memory may be an external storage device or databasefor storing data. Examples may include a hard drive, compact disc(“CD”), digital video disc (“DVD”), memory card, memory stick, floppydisc, universal serial bus (“USB”) memory device, or any other deviceoperative to store data. The memory may be operable to storeinstructions 706 executable by the processor. The functions, acts ortasks illustrated in the figures or described herein may be performed bythe programmed processor executing the instructions stored in thememory. The functions, acts or tasks may be independent of theparticular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firm-ware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

The computer system may further include a display 712, such as a liquidcrystal display (LCD), an organic light emitting diode (OLED), a flatpanel display, a solid state display, a cathode ray tube (CRT), aprojector, a printer or other now known or later developed displaydevice for outputting determined information. The display may act as aninterface for the user to see the functioning of the processor, orspecifically as an interface with the software stored in the memory orin the drive unit 708.

Additionally, the computer system may include an input device 714configured to allow a user to interact with any of the components ofsystem. The input device may be a number pad, a keyboard, or a cursorcontrol device, such as a mouse, or a joystick, touch screen display,remote control or any other device operative to interact with thesystem.

The computer system may also include a disk or optical drive unit. Thedisk drive unit may include a computer-readable medium in which one ormore sets of instructions, e.g. software, may be embedded. Further, theinstructions may perform one or more of the methods or logic asdescribed herein. The instructions may reside completely, or at leastpartially, within the memory and/or within the processor duringexecution by the computer system. The memory and the processor also mayinclude computer-readable media as discussed above.

The present disclosure contemplates a computer-readable medium thatincludes instructions or receives and executes instructions responsiveto a propagated signal, so that a device connected to a network 716 maycommunicate voice, video, audio, images or any other data over thenetwork. Further, the instructions may be transmitted or received overthe network via a communication interface 718. The communicationinterface may be a part of the processor or may be a separate component.The communication interface may be created in software or may be aphysical connection in hardware. The communication interface may beconfigured to connect with a network, external media, the display, orany other components in system, or combinations thereof. The connectionwith the network may be a physical connection, such as a wired Ethernetconnection or may be established wirelessly as discussed below.Likewise, the additional connections with other components of the systemmay be physical connections or may be established wirelessly. In thecase of a service provider server, the service provider server maycommunicate with users through the communication interface.

The network may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, thenetwork may be a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to TCP/IP based networking protocols.

The computer-readable medium may be a single medium, or thecomputer-readable medium may be a single medium or multiple media, suchas a centralized or distributed database, and/or associated caches andservers that store one or more sets of instructions. The term“computer-readable medium” may also include any medium that may becapable of storing, encoding or carrying a set of instructions forexecution by a processor or that may cause a computer system to performany one or more of the methods or operations disclosed herein.

The computer-readable medium may include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. The computer-readable medium also may be a randomaccess memory or other volatile re-writable memory. Additionally, thecomputer-readable medium may include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that may be a tangible storage medium. The computer-readablemedium is preferably a tangible storage medium. Accordingly, thedisclosure may be considered to include any one or more of acomputer-readable medium or a distribution medium and other equivalentsand successor media, in which data or instructions may be stored.

Alternatively or in addition, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, may be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments may broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that may be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system may encompass software, firmware, and hardwareimplementations.

The methods described herein may be implemented by software programsexecutable by a computer system. Further, implementations may includedistributed processing, component/object distributed processing, andparallel processing. Alternatively or in addition, virtual computersystem processing maybe constructed to implement one or more of themethods or functionality as described herein.

Although components and functions are described that may be implementedin particular embodiments with reference to particular standards andprotocols, the components and functions are not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, andHTTP) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

The illustrations described herein are intended to provide a generalunderstanding of the structure of various embodiments. The illustrationsare not intended to serve as a complete description of all of theelements and features of apparatus, processors, and systems that utilizethe structures or methods described herein. Many other embodiments maybe apparent to those of skill in the art upon reviewing the disclosure.Other embodiments may be utilized and derived from the disclosure, suchthat structural and logical substitutions and changes may be madewithout departing from the scope of the disclosure. Additionally, theillustrations are merely representational and may not be drawn to scale.Certain proportions within the illustrations may be exaggerated, whileother proportions may be minimized. Accordingly, the disclosure and thefigures are to be regarded as illustrative rather than restrictive.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe true spirit and scope of the description. Thus, to the maximumextent allowed by law, the scope is to be determined by the broadestpermissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

1.-32. (canceled)
 33. A method comprising: receiving transactional dataassociated with a plurality of merchants and a plurality of customers,the transactional data comprising a plurality of element sets, eachelement set comprising data elements representative of a customer, amerchant, and a timestamp; receiving social network data associated withthe plurality of customers via an application programming interface; foreach customer of the plurality of customers, generating, via aprocessor, one or more networks, wherein generation of each network ofthe one or more networks is performed by: (1) determining transactiondata, from the transactional data, for a selected customer of theplurality of customers; (2) determining one or more transactionmerchants with whom the selected customer has transacted based on thetransaction data; and (3) computing a set of additional customers, eachof which having subsequently transacted with the one or more transactionmerchants with whom the selected customer transacted with, wherein eachnetwork of the one or more networks comprises one or more merchantnodes, a plurality of customer nodes, one or more merchant-customeredges between at least one of the one or more merchant nodes and atleast one of the plurality of customer nodes, one or morecustomer-customer edges between two or more customer nodes of theplurality of customer nodes, and a plurality of weight values eachassociated with at least one of the merchant-customer edges or the oneor more customer-customer edges, wherein the plurality of weight valuesare derived based at least in part on the social network data;generating, via the processor, a network ranking of a particularcustomer node of the plurality of customer nodes based at least in parton a centrality of the particular customer node within at least one ofthe one or more networks, and wherein the centrality is determined atleast in part based on the plurality of weight values; and transmittinga promotion to a particular customer based on the network ranking of theparticular customer node satisfying a predetermined threshold.
 34. Themethod of claim 33 further comprising: for each network of the one ormore networks, determining a link analysis score for each merchant; anddetermining a score summary representative of a degree of influence thatthe particular customer has over the network based on an informationfusion criteria associated with the link analysis score.
 35. The methodof claim 34 further comprising: generating the network ranking of theparticular customer node based on an information fusion criteriaassociated with one or more score summaries determined for the one ormore networks.
 36. The method of claim 33 further comprising:determining an effective cost of the promotion based on the networkranking.
 37. The method of claim 33, wherein the plurality of weightvalues are derived based at least in part on an expected geographicaldistance between respective ones of the one or more merchant nodes andthe plurality of customer nodes associated with the at least one of themerchant-customer edges or the one or more customer-customer edges. 38.The method of claim 33, wherein the social network data comprises anumber of check-ins using a social network associated with the pluralityof customer nodes with respect to the one or more merchant nodes. 39.The method of claim 33, further comprising: determining a plurality ofcustomers influenced by a plurality of particular customers associatedwith network ranking score equal to or greater than the predeterminedthreshold; determining a least number of the plurality of particularcustomers that provide a greatest number of the determined plurality ofcustomers influenced by the plurality of particular customers; andtransmitting the promotion to selected ones of the plurality ofparticular customers based on the determination of the least number ofthe plurality of particular customers.
 40. The method of claim 39,wherein determining the least number of the plurality of particularcustomers further comprises determining a least number of the selectedones of plurality of particular customers that provide a greatest numberof the determined plurality of customers influenced by the plurality ofparticular customers and are common to the selected ones of theplurality of particular customers.
 41. A computer program productcomprising at least one non-transitory computer-readable storage mediumhaving computer-executable program code instructions stored therein, thecomputer-executable program code instructions comprising program codeinstructions for: receiving transactional data associated with aplurality of merchants and a plurality of customers, the transactionaldata comprising a plurality of element sets, each element set comprisingdata elements representative of a customer, a merchant, and a timestamp;receiving social network data associated with the plurality of customersvia an application programming interface; for each customer of theplurality of customers, generating one or more networks, whereingeneration of each network of the one or more networks is performed by:(1) determining transaction data, from the transactional data, for aselected customer of the plurality of customers; (2) determining one ormore transaction merchants with whom the selected customer hastransacted based on the transaction data; and (3) computing a set ofadditional customers, each of which having subsequently transacted withthe one or more transaction merchants with whom the selected customertransacted with, wherein each network of the one or more networkscomprises one or more merchant nodes, a plurality of customer nodes, oneor more merchant-customer edges between at least one of the one or moremerchant nodes and at least one of the plurality of customer nodes, oneor more customer-customer edges between two or more customer nodes ofthe plurality of customer nodes, and a plurality of weight values eachassociated with at least one of the merchant-customer edges or the oneor more customer-customer edges, wherein the plurality of weight valuesare derived based at least in part on the social network data;generating a network ranking of a particular customer node of theplurality of customer nodes based at least in part on a centrality ofthe particular customer node within at least one of the one or morenetworks, and wherein the centrality is determined at least in partbased on the plurality of weight values; and transmitting a promotion toa particular customer based on the network ranking of the particularcustomer node satisfying a predetermined threshold.
 42. The computerprogram product of claim 41 further comprising program code instructionsfor: for each network of the one or more networks, determining a linkanalysis score for each merchant; and determining a score summaryrepresentative of a degree of influence that the particular customer hasover the network based on an information fusion criteria associated withthe link analysis score.
 43. The computer program product of claim 42further comprising program code instructions for generating the networkranking of the particular customer node based on an information fusioncriteria associated with one or more score summaries determined for theone or more networks.
 44. The computer program product of claim 41,wherein the plurality of weight values are derived based at least inpart on an expected geographical distance between respective ones of theone or more merchant nodes and the plurality of customer nodesassociated with the at least one of the merchant-customer edges or theone or more customer-customer edges.
 45. The computer program product ofclaim 41, wherein the social network data comprises a number ofcheck-ins using a social network associated with the plurality ofcustomer nodes with respect to the one or more merchant nodes.
 46. Thecomputer program product of claim 41 further comprising program codeinstructions for: determining a plurality of customers influenced by aplurality of particular customers associated with network ranking scoreequal to or greater than the predetermined threshold; determining aleast number of the plurality of particular customers that provide agreatest number of the determined plurality of customers influenced bythe plurality of particular customers; and transmitting the promotion toselected ones of the plurality of particular customers based on thedetermination of the least number of the plurality of particularcustomers.
 47. The computer program product of claim 46, furthercomprising program code instructions for determining a least number ofthe selected ones of plurality of particular customers that provide agreatest number of the determined plurality of customers influenced bythe plurality of particular customers and are common to the selectedones of the plurality of particular customers.
 48. An apparatuscomprising at least one processor and at least one memory includingcomputer program code, the at least one memory and the computer programcode configured to, with the processor, cause the apparatus to at least:receive transactional data associated with a plurality of merchants anda plurality of customers, the transactional data comprising a pluralityof element sets, each element set comprising data elementsrepresentative of a customer, a merchant, and a timestamp; receivesocial network data associated with the plurality of customers via anapplication programming interface; for each customer of the plurality ofcustomers, generate one or more networks, wherein generation of eachnetwork of the one or more networks is performed by: (1) determiningtransaction data, from the transactional data, for a selected customerof the plurality of customers; (2) determining one or more transactionmerchants with whom the selected customer has transacted based on thetransaction data; and (3) computing a set of additional customers, eachof which having subsequently transacted with the one or more transactionmerchants with whom the selected customer transacted with, wherein eachnetwork of the one or more networks comprises one or more merchantnodes, a plurality of customer nodes, one or more merchant-customeredges between at least one of the one or more merchant nodes and atleast one of the plurality of customer nodes, one or morecustomer-customer edges between two or more customer nodes of theplurality of customer nodes, and a plurality of weight values eachassociated with at least one of the merchant-customer edges or the oneor more customer-customer edges, wherein the plurality of weight valuesare derived based at least in part on the social network data; generatea network ranking of a particular customer node of the plurality ofcustomer nodes based at least in part on a centrality of the particularcustomer node within at least one of the one or more networks, andwherein the centrality is determined at least in part based on theplurality of weight values; and transmit a promotion to a particularcustomer based on the network ranking of the particular customer nodesatisfying a predetermined threshold.
 49. The apparatus of claim 48,wherein the plurality of weight values are derived based at least inpart on an expected geographical distance between respective ones of theone or more merchant nodes and the plurality of customer nodesassociated with the at least one of the merchant-customer edges or theone or more customer-customer edges.
 50. The apparatus of claim 48,wherein the social network data comprises a number of check-ins using asocial network associated with the plurality of customer nodes withrespect to the one or more merchant nodes.
 51. The apparatus of claim 48wherein the at least one memory and the computer program code is furtherconfigured to, with the processor, cause the apparatus to at least:determine a plurality of customers influenced by a plurality ofparticular customers associated with network ranking score equal to orgreater than the predetermined threshold; determine a least number ofthe plurality of particular customers that provide a greatest number ofthe determined plurality of customers influenced by the plurality ofparticular customers; and transmit the promotion to selected ones of theplurality of particular customers based on the determination of theleast number of the plurality of particular customers.
 52. The apparatusof claim 51 wherein the at least one memory and the computer programcode is further configured to, with the processor, cause the apparatusto at least determine a least number of the selected ones of pluralityof particular customers that provide a greatest number of the determinedplurality of customers influenced by the plurality of particularcustomers and are common to the selected ones of the plurality ofparticular customers.